tinygrad/test/models/test_real_world.py

import unittest, time
import numpy as np
from tinygrad.tensor import Tensor
from tinygrad.nn import optim
from tinygrad.nn.state import get_parameters
from tinygrad.jit import TinyJit
from tinygrad import Device, GlobalCounters
from tinygrad.helpers import CI, dtypes
from tinygrad.shape.symbolic import Variable
from test.helpers import derandomize_model

from examples.gpt2 import Transformer as GPT2Transformer, MODEL_PARAMS as GPT2_MODEL_PARAMS
from examples.hlb_cifar10 import SpeedyResNet
from examples.llama import Transformer as LLaMaTransformer, MODEL_PARAMS as LLAMA_MODEL_PARAMS
from examples.stable_diffusion import UNetModel

def helper_test(nm, gen, train, max_memory_allowed, max_kernels_allowed, all_jitted=False):
  tms = []
  for _ in range(4):
    GlobalCounters.reset()
    GlobalCounters.mem_used = 0
    Device[Device.DEFAULT].synchronize()
    st = time.perf_counter_ns()
    train(*gen())
    Device[Device.DEFAULT].synchronize()
    tms.append(time.perf_counter_ns() - st)

  # TODO: jit should expose this correctly with graph
  kernels_used = len(train.jit_cache) if hasattr(train, "jit_cache") else None
  print(f"{nm}: used {GlobalCounters.mem_used/1e9:.2f} GB and {kernels_used} kernels in {min(tms)/1e6:.2f} ms")
  assert GlobalCounters.mem_used/1e9 < max_memory_allowed, f"{nm} used more than {max_memory_allowed:.2f} GB"
  assert not kernels_used or kernels_used <= max_kernels_allowed, f"{nm} used more than {max_kernels_allowed} kernels"
  if all_jitted:
    assert kernels_used > 0 and kernels_used == GlobalCounters.kernel_count or (kernels_used == 1 and getattr(Device[Device.DEFAULT], "graph", None)), f"only {kernels_used} out of {GlobalCounters.kernel_count} were jitted"

class TestRealWorld(unittest.TestCase):
  def setUp(self):
    self.old_type = Tensor.default_type
    np.random.seed(2002)

  def tearDown(self):
    Tensor.default_type = self.old_type

  @unittest.skipIf(Device.DEFAULT == "LLVM", "LLVM segmentation fault")
  @unittest.skipIf(CI, "too big for CI")
  def test_stable_diffusion(self):
    model = UNetModel()
    derandomize_model(model)
    @TinyJit
    def test(t, t2): return model(t, 801, t2).realize()
    helper_test("test_sd", lambda: (Tensor.randn(1, 4, 64, 64),Tensor.randn(1, 77, 768)), test, 18.0, 953)

  @unittest.skipIf(Device.DEFAULT == "LLVM", "LLVM segmentation fault")
  @unittest.skipIf(Device.DEFAULT in ["LLVM", "GPU"] and CI, "too long on CI LLVM, GPU requires cl_khr_fp1")
  def test_llama(self):
    Tensor.default_type = dtypes.float16

    args_tiny = {"dim": 1024, "hidden_dim": 2048, "n_heads": 8, "n_layers": 8, "norm_eps": 1e-05, "vocab_size": 1000}
    model = LLaMaTransformer(**(args_tiny if CI else LLAMA_MODEL_PARAMS["1"]["7B"]["args"]))
    derandomize_model(model)
    @TinyJit
    def test(t): return model(t, 0).realize()
    # TODO: test first token vs rest properly, also memory test is broken with CacheCollector
    helper_test("test_llama", lambda: (Tensor([[1,2,3,4]]),), test, 0.22 if CI else 13.5, 181 if CI else 685, all_jitted=True)

  @unittest.skipIf(Device.DEFAULT in ["LLVM", "GPU"] and CI, "too long on CI LLVM, GPU requires cl_khr_fp16")
  def test_gpt2(self):
    Tensor.default_type = dtypes.float16

    args_tiny = {"dim": 1024, "n_heads": 8, "n_layers": 8, "norm_eps": 1e-5, "vocab_size": 1000}
    model = GPT2Transformer(**(args_tiny if CI else GPT2_MODEL_PARAMS["gpt2-medium"]))
    derandomize_model(model)
    @TinyJit
    def test(t, v): return model(t, v).realize()
    helper_test("test_gpt2", lambda: (Tensor([[1,]]),Variable("pos", 1, 100).bind(1)), test, 0.21 if CI else 0.9, 180 if CI else 516, all_jitted=True)

  @unittest.skipIf(Device.DEFAULT == "LLVM", "LLVM segmentation fault")
  @unittest.skipIf(Device.DEFAULT in ["LLVM", "CLANG"] and CI, "too long on CI LLVM and CLANG")
  def test_train_cifar(self):
    # TODO: with default device
    #old_default = Device.DEFAULT
    #Device.DEFAULT = "FAKE"
    #Device['fake'].codegen = Device[old_default].codegen

    with Tensor.train():
      model = SpeedyResNet(Tensor.ones((12,3,2,2)))
      optimizer = optim.SGD(get_parameters(model), lr=0.01, momentum=0.8, nesterov=True, weight_decay=0.15)

      BS = 32 if CI else 512

      @TinyJit
      def train(X):
        out = model(X)
        loss = out.mean()
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

      helper_test("train_cifar", lambda: (Tensor.randn(BS, 3, 32, 32),), train, (1.0/48)*BS, 142 if CI else 154)   # it's 154 on metal

      # reset device
      #Device.DEFAULT = old_default

if __name__ == '__main__':
  unittest.main()